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CS:4420 Artificial IntelligenceSpring 2017
Propositional Logic
Cesare Tinelli
The University of Iowa
Copyright 2004–17, Cesare Tinelli and Stuart Russell a
aThese notes were originally developed by Stuart Russell and are used with permission. They are
copyrighted material and may not be used in other course settings outside of the University of Iowa in their
current or modified form without the express written consent of the copyright holders.
CS:4420 Spring 2017 – p.1/49
Readings
• Chap. 7 of [Russell and Norvig, 2012]
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Logics
A logic is a triple 〈L,S,R〉 where
• L, the logic’s language, is a class of sentences described by aformal grammar
• S, the logic’s semantics is a formal specification of how to assignmeaning in the“real world” to the elements of L
• R, the logic’s inference system, is a set of formal derivation rules
over L
There are several logics: propositional, first-order, higher-order, modal,temporal, intuitionistic, linear, equational, non-monotonic, fuzzy, . . .
We will concentrate on propositional logic and first-order logic
CS:4420 Spring 2017 – p.3/49
Propositional Logic
Each sentence is made of
• propositional variables (A,B, . . . , P,Q, . . .)
• logical constants (True,False)
• logical connectives (∧,∨,⇒, . . .)
Every propositional variable stands for a basic fact
Examples: I’m hungry, Apples are red, Joe and Jill are married
CS:4420 Spring 2017 – p.4/49
Propositional Logic
Ontological CommitmentsPropositional Logic is about facts in the world that are either true orfalse, nothing else
Semantics of Propositional LogicSince each propositional variable stands for a fact about the world, itsmeaning ranges over the Boolean values {true, false}
Note: Do note confuse
• true, false, which are values (i.e., semantical entities) here with
• True, False, which are logical constants (i.e., symbols of thelanguage)
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Propositional Logic
The Language
• Each propositional variable (A,B, . . . , P,Q, . . .) is a sentence
• Each logical constant (True,False) is a sentence
• If ϕ and ψ are sentences, all of the following are also sentences
(ϕ) ¬ϕ ϕ ∧ ψ ϕ ∨ ψ ϕ⇒ ψ ϕ⇔ ψ
• Nothing else is a sentence
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The Language of Propositional Logic
More formally, it is the language generated by the following grammar
Symbols:
• Propositional variables: A,B, . . . , P,Q, . . .
• Logical constants:
True (true) ∧ (and) ⇒ (implies) ¬ (not)
False (false) ∨ (or) ⇔ (equivalent)
Grammar Rules:
Sentence ::= AtomicS | ComplexS
AtomicS ::= True | False | A | B | . . . | P | Q | . . .
ComplexS ::= (Sentence) | Sentence Connective Sentence | ¬Sentence
Connective ::= ∧ | ∨ | ⇒ | ⇔
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Wumpus world sentences
Let Pi,j be true if there is a pit in [i, j]
Let Bi,j be true if there is a breeze in [i, j]
“Pits cause breezes in adjacent squares”
¬P1,1
¬A
B2,1
CS:4420 Spring 2017 – p.8/49
Wumpus world sentences
Let Pi,j be true if there is a pit in [i, j]
Let Bi,j be true if there is a breeze in [i, j]
“Pits cause breezes in adjacent squares”
¬P1,1
¬A
B2,1
“A square is breezy if and only if there is an adjacent pit”
A ⇔ (B ∨ C)
B2,1 ⇔ (P1,1 ∨ P2,2 ∨ P3,1)
CS:4420 Spring 2017 – p.8/49
Semantics of Propositional Logic
The meaning of True is always trueThe meaning of False is always false
The meaning of the other sentences depends on the meaning of thepropositional variables
• Base cases: truth tables
P Q ¬P P ∧Q P ∨Q P⇒Q P⇔Q
false false true false false true true
false true true false true true false
true false false false true false false
true true false true true true true
• Non-base Cases: given by reduction to the base cases
Ex: the meaning of (P ∨Q) ∧R is the same as the meaning ofA ∧R where A has the same meaning as P ∨Q
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Semantics of Propositional Logic
An assignment of Boolean values to the propositional variables of asentence is an interpretation of the sentence
P H P _H (P _H) ^:H ((P _H) ^ :H) ) P
False False False False TrueFalse True True False TrueTrue False True True TrueTrue True True False True
Interpretations: {P 7→ false, H 7→ false}, {P 7→ false, H 7→ true}, . . .
The semantics of Propositional Logic is compositional:the meaning of a sentence is defined recursively in terms of themeaning of the sentence’s components
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Semantics of Propositional Logic
The meaning of a sentence in general depends on its interpretation
Some sentences, however, have always the same meaning
P H P _H (P _H) ^:H ((P _H) ^ :H) ) P
False False False False TrueFalse True True False TrueTrue False True True TrueTrue True True False True
A sentence is
• satisfiable if it is true in some interpretation
• unsatisfiable if it is true in no interpretation
• valid if it is true in every possible interpretation
• invalid if it is false in some possible interpretation
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A Warning
Disjunction
• A ∨B is true when A or B or or both are true (inclusive or)
• A⊕B is sometimes used to mean“either A or B but not both”(exclusive or)
Implication
• A⇒ B does not require a causal connection between A and BEx: Sky-is-blue ⇒ Snow-is-white
• When A is false, A⇒ B is always true regardless of BEx: Two-equals-four ⇒ Apples-are-red
• Beware of negations in implicationsEx: Is-a-female-bird ⇒ Lays-eggs
¬Is-a-female-bird ⇒ ¬lays-eggs
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Entailment in Propositional Logic
Given
• a set Γ of sentences and
• a sentence ϕ,
we write
Γ |= ϕ
iff every interpretation that makes all sentences in Γ true makes ϕ alsotrue
Γ |= ϕ is read as“Γ entails ϕ”or“ϕ logically follows from Γ”
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Entailment in Propositional Logic
Examples
{A,A⇒ B} |= B
{A} |= A ∨B
{A,B} |= A ∧B
{} |= A ∨ ¬A
{A} 6|= A ∧B
{A ∨ ¬A} 6|= A
A B A⇒ B A ∨B A ∧B A ∨ ¬A
1. false false true false false true
2. false true true true false true
3. true false false true false true
4. true true true true true true
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Properties of Entailment
• Γ |= ϕ, for all ϕ ∈ Γ (inclusion property of PL)
• if Γ |= ϕ, then Γ′ |= ϕ for all Γ′ ⊇ Γ (monotonicity of PL)
• ϕ is valid iff { } |= ϕ (also written as |= ϕ)
• ϕ is unsatisfiable iff ϕ |= False
• Γ |= ϕ iff the set Γ ∪ {¬ϕ} is unsatisfiable
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Logical Equivalence
Two sentences ϕ1 and ϕ2 are logically equivalent, written
ϕ1 ≡ ϕ2
if ϕ1 |= ϕ2 and ϕ2 |= ϕ1
Note:
• ϕ1 ≡ ϕ2 if and only if every interpretation assigns the sameBoolean value to ϕ1 and ϕ2
• Implication and equivalence (⇒, ⇔), which are syntacticalentities, are intimately related to entailment and logicalequivalence (|=, ≡), which are semantical notions:
ϕ1 |= ϕ2 iff |= ϕ1 ⇒ ϕ2
ϕ1 ≡ ϕ2 iff |= ϕ1 ⇔ ϕ2
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Properties of Logical Connectives
• ∧ and ∨ are commutativeϕ1 ∧ ϕ2 ≡ ϕ2 ∧ ϕ1
ϕ1 ∨ ϕ2 ≡ ϕ2 ∨ ϕ1
• ∧ and ∨ are associative
ϕ1 ∧ (ϕ2 ∧ ϕ3) ≡ (ϕ1 ∧ ϕ2) ∧ ϕ3
ϕ1 ∨ (ϕ2 ∨ ϕ3) ≡ (ϕ1 ∨ ϕ2) ∨ ϕ3
• ∧ and ∨ are mutually distributive
ϕ1 ∧ (ϕ2 ∨ ϕ3) ≡ (ϕ1 ∧ ϕ2) ∨ (ϕ1 ∧ ϕ3)
ϕ1 ∨ (ϕ2 ∧ ϕ3) ≡ (ϕ1 ∨ ϕ2) ∧ (ϕ1 ∨ ϕ3)
• ∧ and ∨ are related by ¬ (DeMorgan’s Laws)
¬(ϕ1 ∧ ϕ2) ≡ ¬ϕ1 ∨ ¬ϕ2
¬(ϕ1 ∨ ϕ2) ≡ ¬ϕ1 ∧ ¬ϕ2
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Properties of Logical Connectives
∧, ⇒, and ⇔ are actually redundant:
ϕ1 ∧ ϕ2 ≡ ¬(¬ϕ1 ∨ ¬ϕ2)
ϕ1 ⇒ ϕ2 ≡ ¬ϕ1 ∨ ϕ2
ϕ1 ⇔ ϕ2 ≡ (ϕ1 ⇒ ϕ2) ∧ (ϕ2 ⇒ ϕ1)
We keep them all mainly for convenience
Exercise Use the truth tables to verify all the logical equivalences seenso far
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Inference Systems for Propositional Logic
An inference system I for PL is a procedure that
given a set Γ = {α1, . . . , αm} of sentences and a sentence ϕ,may reply“yes”,“no”, or run forever
If I replies positively to input (Γ, ϕ), we say that Γ derives ϕ in I (or,I derives ϕ from Γ, or, ϕ derives from Γ in I) and write
Γ ⊢I ϕ
Intuitively, I should be such that it replies“yes”on input (Γ, ϕ)only if ϕ is in fact entailed by Γ
CS:4420 Spring 2017 – p.19/49
All These Fancy Symbols!
Note:
A ∧B ⇒ C is a sentence, a bunch of symbols manipulated by aninference system I
A∧B |= C is a mathematical abbreviation standing for the statement:“every interpretation that makes A ∧B true, makes C also true”
A ∧B ⊢I C is a mathematical abbreviation standing for thestatement: “I derives C from A ∧B”
In other words,
⇒ is a formal symbol of the logic, which is used by the inferencesystem
|= is a shorthand we use to talk about the meaning of formalsentences
⊢I is a shorthand we use to talk about the output of the inferencesystem I
CS:4420 Spring 2017 – p.20/49
All These Fancy Symbols!
The connective ⇒ and the shorthand |= are related as follows
The sentence ϕ1 ⇒ ϕ2 is valid (always true) if and only if ϕ1 |= ϕ2
Example: A⇒ (A ∨B) is valid and A |= (A ∨B)
A B A ∨B A⇒ (A ∨B)
1. false false false true
2. false true true true
3. true false true true
4. true true true true
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All These Fancy Symbols!
The shorthands |= and ⊢I are related as follows
• A sound inference system can derive only sentences that logicallyfollow from a given set of sentences:
if Γ ⊢I ϕ then Γ |= ϕ
• A complete inference system can derive all sentences thatlogically follow from a given set of sentences:
if Γ |= ϕ then Γ ⊢I ϕ
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Inference systems for PL
Divided into (roughly) two kinds:
Rule-based
• Sound generation of new sentences from old
• Proof = a sequence of inference rule applicationsCan use inference rules as operators as in a standard searchprocedures
• Typically require translation of sentences into some normal form
Model-based
• Truth table enumeration (always exponential in n)
• Improved backtracking, e.g., Davis–Putnam–Logemann–Loveland
• Heuristic search in model space (incomplete)e.g., min-conflicts-like hill-climbing algorithms
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Truth table enumeration
The proof system T T is specified as follows:
{α1, . . . , αm} ⊢T T ϕ iff all the values in the truth table of
(α1 ∧ · · · ∧ αm)⇒ ϕ are true
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Inference by Truth Tables• The truth-tables-based inference system is sound:
α1, . . . , αm ⊢T T ϕ implies truth table of (α1 ∧ · · · ∧ αm) ⇒ ϕ all true
implies (α1 ∧ · · · ∧ αm) ⇒ ϕ is valid
implies |= (α1 ∧ · · · ∧ αm) ⇒ ϕ
implies (α1 ∧ · · · ∧ αm) |= ϕ
implies α1, . . . , αm |= ϕ
• It is also complete (exercise: prove it)
• Its time complexity is O(2n) where n is the number ofpropositional variables in α1, . . . , αm, ϕ
• We cannot hope to do better in general because the dualproblem: determining the satisfiability of a sentence, isNP-complete
• However, really hard cases of propositional inference aresomewhat rare in practice
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Rule-Based Inference in PL
An inference system in Propositional Logic can also be specified as aset R of inference (or derivation) rules
• Each rule is just a pattern premises/conclusion
• A rule applies to Γ and derives ϕ if• some of the sentences in Γ match with the premises of the
rule and
• ϕ matches with the conclusion
• A rule is sound it the set of its premises entails its conclusion
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Some Inference Rules
• And-Introduction
α β
α ∧ β
• And-Eliminationα ∧ β
α
α ∧ β
β
• Or-Introductionα
α ∨ β
α
β ∨ α
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Some Inference Rules (cont’d)
• Implication-Elimination (aka Modus Ponens)
α⇒ β α
β
• Unit Resolutionα ∨ β ¬β
α
• Resolutionα ∨ β ¬β ∨ γ
α ∨ γor, equivalently,
¬α⇒ β, β ⇒ γ
¬α⇒ γ
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Some Inference Rules (cont’d)
• Double-Negation-Elimination
¬¬α
α
• False-Introductionα ∧ ¬α
False
• False-Elimination
False
β
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Inference by Proof
We say there is a proof of ϕ from Γ in an inference system R if wecan derive ϕ by applying the rules of R repeatedly to Γ and its derivedsentences
Example: a proof of P from {(P ∨H) ∧ ¬H}
1. (P ∨H) ∧ ¬H by assumption
2. P ∨H by ∧-elimination applied to (1)
3. ¬H by ∧-elimination applied to (1)
4. P by unit resolution applied to (2),(3)
We can represent a proof more visually as a proof tree:
Example:
(P ∨H) ∧ ¬H
P ∨H
(P ∨H) ∧ ¬H
¬H
P
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Rule-Based Inference in Propositional Logic
More precisely, there is a proof of ϕ from Γ in R if
1. ϕ ∈ Γ or,
2. there is a rule in R that applies to Γ and produces ϕ or,
3. there is a proof of each ϕ1, . . . , ϕm from Γ in R anda rule that applies to {ϕ1, . . . , ϕm} and produces ϕ
Then, the inference system R is specified as follows:
Γ ⊢R ϕ iff there is a proof of ϕ from Γ in R
CS:4420 Spring 2017 – p.31/49
An Inference System R
α β
α ∧ β
α
α ∨ β
α
β ∨ α
α ∧ β
α
α ∧ β
β
α⇒ β α
β
α ∨ β ¬β
α
α ∨ β ¬β ∨ γ
α ∨ γ
¬¬α
α
α ∧ ¬α
False
False
β
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Soundness of R
The given system R is sound because all of its rules are
Example: the Resolution ruleα ∨ β, ¬β ∨ γ
α ∨ γ
α β γ ¬β α ∨ β ¬β ∨ γ α ∨ γ
1. false false false true false true false
2. false false true true false true true
3. false true false false true false false
4. false true true false true true true
5. true false false true true true true
6. true false true true true true true
7. true true false false true false true
8. true true true false true true true
All the interpretations that satisfy both α ∨ β and ¬β ∨ γ (4,5,6,8) satisfy α ∨ γ as well
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Soundness of R
The given system R is sound because all of its rules are
Exercise: prove that the other inference rules are sound as well
Is R also complete?
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Resolution
Literal: prop. symbol (P ) or negated prop. symbol (¬P )
Clause: set of literals {l1, . . . , lk} (understood as l1 ∨ · · · ∨ lk)
Conjunctive Normal Form: set of clauses {C1, . . . , Cn} (understood asC1 ∧ · · · ∧ Cn)
Resolution rule for CNF:
l1 ∨ · · · ∨ lk ∨ P ¬P ∨m1 ∨ · · · ∨mn
l1 ∨ · · · ∨ lk ∨m1 ∨ · · · ∨mn
E.g.,
P ∨Q ¬Q
P
P ∨Q R ∨ ¬Q ∨ ¬S
P ∨R ∨ ¬S
P ∨Q ¬Q ∨ P ∨R
P ∨R
Resolution is sound and complete for CNF KBs
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Conversion to CNF
Ex.: A⇔ (B ∨ C)
1. Eliminate ⇔, replacing α⇔ β with (α⇒ β) ∧ (β ⇒ α)
(A⇒ (B ∨ C)) ∧ ((B ∨ C)⇒ A)
2. Eliminate ⇒, replacing α⇒ β with ¬α ∨ β
(¬A ∨B ∨ C) ∧ (¬(B ∨ C) ∨ A)
3. Move ¬ inwards using de Morgan’s rules and double-negation
(¬A ∨B ∨ C) ∧ ((¬B ∧ ¬C) ∨ A)
4. Apply distributivity law (∨ over ∧) and flatten
(¬A ∨B ∨ C) ∧ (¬B ∨A) ∧ (¬C ∨A)
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Resolution Procedure
Proof by contradiction, i.e., show KB ∧ ¬α unsatisfiable
function PL-Resolution(KB,α) returns true or false
clauses← the set of clauses in the CNF representation of KB ∧ ¬α
new←{}
loop do
for each Ci, Cj in clauses do
resolvents←PL-Resolve(Ci,Cj)
if resolvents contains the empty clause then return true
new←new ∪ resolvents
if new ⊆ clauses then return false
clauses← clauses ∪ new
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Resolution example
KB = { B1,1 ⇔ (P1,2 ∨ P2,1), ¬B1,1 }
α = ¬P1,2
CNF = { ¬P1,2 ∨B1,1, ¬B1,1 ∨ P1,2 ∨ P2,1, ¬P1,2 ∨B1,1,
¬B1,1, P1,2}
P1,2
P1,2
P2,1
P1,2 B1,1
B1,1 P2,1 B1,1 P1,2 P2,1 P2,1P1,2B1,1 B1,1
P1,2B1,1 P2,1B1,1P2,1 B1,1
P1,2 P2,1 P1,2
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Forward and backward chaining
Horn clause: prop. symbol (p) or implication p1 ∧ · · · ∧ pn ⇒ p
Horm Form: set of Horn clauses {C1, . . . , Cn} (understood asC1 ∧ · · · ∧ Cn)
E.g., { C, B ⇒ A, C ∧D ⇒ B }
Modus Ponens for Horn Form
α1 · · · αn α1 ∧ · · · ∧ αn ⇒ β
β
Sound and complete for Horn Form KBs
Can be used with forward chaining or backward chaining
These algorithms are very natural and run in linear time
CS:4420 Spring 2017 – p.38/49
Forward chaining
Idea:Fire any rule whose premises are satisfied in the KB,add its conclusion to the KB, until query is found
Ex.:
P =⇒ Q
L ∧M =⇒ P
B ∧ L =⇒ M
A ∧ P =⇒ L
A ∧B =⇒ L
A
B
Q
P
M
L
BA
CS:4420 Spring 2017 – p.39/49
Forward chaining algorithm
function PL-FC-Entails?(KB, q) returns true or false
count, a table, indexed by clause, initially the number of premises
inferred, a table, indexed by symbol, each entry initially false
agenda, a list of symbols, initially the symbols known to be true
while agenda is not empty do
p←Pop(agenda)
unless inferred[p] do
inferred[p]← true
for each Horn clause c in whose premise p appears do
decrement count[c]
if count[c] = 0 then do
if Head[c] = q then return true
Push(Head[c],agenda)
return false
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Forward chaining example
Q
P
M
L
BA
2 2
2
2
1
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Forward chaining example
Q
P
M
L
B
2
1
A
1 1
2
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Forward chaining example
Q
P
M
2
1
A
1
B
0
1L
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Forward chaining example
Q
P
M
1
A
1
B
0
L0
1
CS:4420 Spring 2017 – p.41/49
Forward chaining example
Q
1
A
1
B
0
L0
M
0
P
CS:4420 Spring 2017 – p.41/49
Forward chaining example
Q
A B
0
L0
M
0
P
0
0
CS:4420 Spring 2017 – p.41/49
Proof of completeness
FC derives every atomic sentence that is entailed by KB
1. FC reaches a fixed point where no new atomic sentences(prop. symbols) are inferred
2. Consider the final state as a model m, assigning true to theinferred symbols and false to the other symbols
3. Claim: Every clause in the original KB is satisfied by m
Proof: Suppose a clause a1 ∧ . . . ∧ ak ⇒ b is falsified by mThen a1 ∧ . . . ∧ ak is satisfied by m while b is notBut then b what not inferred, contradicting the assumption thatthe algorithm had reached a fixed point!
4. Hence m is a model of KB
5. 5. If KB |= q, q is true in every model of KB, including m
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Backward chaining
Idea: work backwards from the query q
to infer q by BC,check if q is known already, orinfer by BC all premises of some rule concluding q
Avoid loops: check if new subgoal is already on the goal stack
Avoid repeated work: check if new subgoal
1. has already been inferred, or
2. has already failed
CS:4420 Spring 2017 – p.43/49
Backward chaining example
Q
P
M
L
A B
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Backward chaining example
P
M
L
A
Q
B
CS:4420 Spring 2017 – p.44/49
Backward chaining example
M
L
A
Q
P
B
CS:4420 Spring 2017 – p.44/49
Backward chaining example
M
A
Q
P
L
B
CS:4420 Spring 2017 – p.44/49
Backward chaining example
M
A
Q
P
L
B
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Backward chaining example
M
A
Q
P
L
B
CS:4420 Spring 2017 – p.44/49
Backward chaining example
A
Q
P
L
B
M
CS:4420 Spring 2017 – p.44/49
Backward chaining example
A
Q
P
L
B
M
CS:4420 Spring 2017 – p.44/49
Backward chaining example
A
Q
P
L
B
M
CS:4420 Spring 2017 – p.44/49
Backward chaining example
A
Q
P
L
B
M
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Forward vs. Backward Chaining
FC is data-driven, cf. automatic, unconscious processing
e.g., object recognition, routine decisions
May do lots of work that is irrelevant to the goal
BC is goal-driven, appropriate for problem-solving,
e.g., Where are my keys? How do I get into a PhD program?
Complexity of BC can be much less than linear in size of KB
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Model Checking methods
The most effective procedures for propositional satisfiability are basedon CSP techniques
Variable domain: {true, false}
Constraints: sets of clauses
Heuristic Improvements:
• unit propagation
• variable and value ordering
• intelligent backtracking
• clause learning
• random restarts
• clever indexing
• subproblem decomposition
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DPLL Procedure
function DPLL-Satisfiable?(s) returns true or false
inputs: s, a sentence in propositional logic
clauses← the set of clauses in the CNF representation of s
symbols← a list of the proposition symbols in s
return DPLL(clauses, symbols, [ ])
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DPLL Procedure (cont.)
function DPLL(clauses, symbols,model) returns true or false
if every clause in clauses is satisfied by model then return true
if some clause in clauses is falsified by model then return false
P, value←Find-Pure-Symbol(symbols, clauses,model)
if P is non-null then
return DPLL(clauses, symbols - P, (P 7→ value) :: model)
P, value←Find-Unit-Clause(clauses,model)
if P is non-null then
return DPLL(clauses, symbols - P, (P 7→ value) :: model)
P←First(symbols)
rest←Rest(symbols)
return DPLL(clauses, rest, (P 7→ true) :: model) or
DPLL(clauses, rest, (P 7→ false) :: model)
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DPLL Exercise
(1) ¬p1 ∨ p2 (2) ¬p3 ∨ p4 (3) ¬p6 ∨ ¬p5 ∨ ¬p2
(4) ¬p5 ∨ p6 (5) p5 ∨ p7 (6) ¬p1 ∨ p5 ∨ ¬p7
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